Adaptive Noise Reduction Algorithm for Speech Enhancement
In this paper, Least Mean Square (LMS) adaptive
noise reduction algorithm is proposed to enhance the speech signal
from the noisy speech. In this, the speech signal is enhanced by
varying the step size as the function of the input signal. Objective and
subjective measures are made under various noises for the proposed
and existing algorithms. From the experimental results, it is seen that
the proposed LMS adaptive noise reduction algorithm reduces Mean
square Error (MSE) and Log Spectral Distance (LSD) as compared to
that of the earlier methods under various noise conditions with
different input SNR levels. In addition, the proposed algorithm
increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR
improvement (ΔSNRseg) values; improves the Mean Opinion Score
(MOS) as compared to that of the various existing LMS adaptive
noise reduction algorithms. From these experimental results, it is
observed that the proposed LMS adaptive noise reduction algorithm
reduces the speech distortion and residual noise as compared to that
of the existing methods.
[1] T. Aboulnasr and K. Mayyas, “A robust variable step-size LMS-type
algorithm: analysis and simulations”, IEEE Transactions on Signal
Processing, vol. 45 no.3, pp.631 – 639, 1997.
[2] I.Almajai and B.Milner, “Visually Derived Wiener Filters for Speech
Enhancement”, IEEE Transactions on Audio, Speech and Language
Processing, vol. 19 no.6, pp.1642 – 1651, 2011.
[3] F.Asano, S.Hayamizu, T.Yamada and S.Nakamura, “Speech
Enhancement Based on the Subspace Method”, IEEE transactions on
Speech and Audio Processing, vol. 8 no.5, pp.97-507, 2000.
[4] J.Benesty, H.Rey, L.R.Vega and S.Tressens, “A Nonparametric VSS
NLMS Algorithm”, IEEE Signal Processing Letters, vol. 13, no.10, pp.
581-584, 2006.
[5] J.Benesty and Y.Huang, Adaptive Signal Processing: Applications to
Real-world Problems. Berlin, Springer, 2003.
[6] N.J.Bershad, “Analysis of the Normalized LMS Algorithm with
Gaussian Inputs”, IEEE Transactions on Acoustics, Speech and Signal
Processing, vol. ASSP-34, no.4, pp.93-806, 1986.
[7] S.Boll, “Suppression of acoustic noise in speech using spectral
subtraction”, IEEE Transactions on Acoustics, Speech and Signal
Processing, vol. ASSP-27, no.2, pp.113–120,1979.
[8] H.F.Chi, S.X.Gao, S.D.Soli and A.Alwan, “Band-limited feedback
cancellation with a modified Filtered-X LMS algorithm for hearing
aids”, Speech Communication, vol. 39, pp.147–161, 2003.
[9] B.Cornelis, M.Moonen and J.Wouters, “Performance Analysis of
Multichannel Wiener Filter-Based Noise Reduction in Hearing Aids
under Second Order Statistics Estimation Errors”, IEEE Transactions on
Audio, Speech and Language Processing, vol. 19, no.5, pp.1368-1381,
2011.
[10] S.C.Douglas, “Fast Implementations of the Filtered-X LMS and LMS
Algorithms for Multichannel Active Noise Control”, IEEE Transactions
on Speech and Audio Processing, vol. 7, no.4, pp.454-465, 1999.
[11] M.A.A.El-Fattah, M.I.Dessouky, et al., “Speech enhancement with an
adaptive Wiener filter”, International Journal on Speech Technology,
vol. 17, pp.53–64, 2014.
[12] S.Haykin, Adaptive Filter Theory. Prentice-Hall, 2001.
[13] S.Haykin, Least-Mean-Square Adaptive Filters. Wiley, 2003.
[14] J.Hellgren, “Analysis of Feedback Cancellation in Hearing Aids with
Filtered-X LMS and the Direct Method of Closed Loop Identification”,
IEEE Transactions on Speech and Audio Processing, vol. 10, no.2,
pp.119-131, 2002.
[15] B.Huang, Y.Xiao, J.Sun and G.Wei, “A Variable Step-Size Fx-LMS
Algorithm for Narrowband Active Noise Control”, IEEE Transactions
on Audio, Speech, and Language Processing, vol. 21, no.2, pp.301-312,
2013.
[16] H.C.Huang and J.Lee, “A New Variable Step-Size NLMS Algorithm
and Its Performance Analysis”, IEEE Transactions on Signal
Processing, vol. 60, no.4, pp.2055-2060, 2012.
[17] Y.Lu and P.C.Loizou, “A geometric approach to Spectral subtraction”,
Speech Communication, vol. 50, pp.453-466, 2008.
[18] J.R.Mohammed, M.S.Shafi, S.Imtiaz, R.I.Ansari and M.Khan, “An
Efficient Adaptive Noise Cancellation Scheme Using ALE and NLMS
Filters”, International Journal of Electrical and Computer Engineering,
vol. 2, no.3, pp. 325-332, 2012.
[19] B.K.Mohanty and P.K.Meher, “A High Performance Energy-Efficient
Architecture for FIR Adaptive Filter Based on New Distributed
Arithmetic Formulation of Block LMS Algorithm”, IEEE Transactions
on Signal Processing, vol. 61, no.4, pp.921-932, 2013.
[20] R.Serizel, M.Moonen, J.Wouters and S.H.Jensen, “A Zone-of-Quiet
Based Approach to Integrated Active Noise Control and Noise
Reduction for Speech Enhancement in Hearing Aids”, IEEE
Transactions on Audio, Speech and Language Processing, vol. 20, no.6,
pp.1685-1697, 2012.
[21] B.L.Sim, Y.C.Tong, J.Chang and C.T.Tan, “A parametric formulation of
the generalized spectral subtraction method”, IEEE Transactions on
Speech and Audio Processing, vol. 6, no.4, pp.328–337, 1998.
[1] T. Aboulnasr and K. Mayyas, “A robust variable step-size LMS-type
algorithm: analysis and simulations”, IEEE Transactions on Signal
Processing, vol. 45 no.3, pp.631 – 639, 1997.
[2] I.Almajai and B.Milner, “Visually Derived Wiener Filters for Speech
Enhancement”, IEEE Transactions on Audio, Speech and Language
Processing, vol. 19 no.6, pp.1642 – 1651, 2011.
[3] F.Asano, S.Hayamizu, T.Yamada and S.Nakamura, “Speech
Enhancement Based on the Subspace Method”, IEEE transactions on
Speech and Audio Processing, vol. 8 no.5, pp.97-507, 2000.
[4] J.Benesty, H.Rey, L.R.Vega and S.Tressens, “A Nonparametric VSS
NLMS Algorithm”, IEEE Signal Processing Letters, vol. 13, no.10, pp.
581-584, 2006.
[5] J.Benesty and Y.Huang, Adaptive Signal Processing: Applications to
Real-world Problems. Berlin, Springer, 2003.
[6] N.J.Bershad, “Analysis of the Normalized LMS Algorithm with
Gaussian Inputs”, IEEE Transactions on Acoustics, Speech and Signal
Processing, vol. ASSP-34, no.4, pp.93-806, 1986.
[7] S.Boll, “Suppression of acoustic noise in speech using spectral
subtraction”, IEEE Transactions on Acoustics, Speech and Signal
Processing, vol. ASSP-27, no.2, pp.113–120,1979.
[8] H.F.Chi, S.X.Gao, S.D.Soli and A.Alwan, “Band-limited feedback
cancellation with a modified Filtered-X LMS algorithm for hearing
aids”, Speech Communication, vol. 39, pp.147–161, 2003.
[9] B.Cornelis, M.Moonen and J.Wouters, “Performance Analysis of
Multichannel Wiener Filter-Based Noise Reduction in Hearing Aids
under Second Order Statistics Estimation Errors”, IEEE Transactions on
Audio, Speech and Language Processing, vol. 19, no.5, pp.1368-1381,
2011.
[10] S.C.Douglas, “Fast Implementations of the Filtered-X LMS and LMS
Algorithms for Multichannel Active Noise Control”, IEEE Transactions
on Speech and Audio Processing, vol. 7, no.4, pp.454-465, 1999.
[11] M.A.A.El-Fattah, M.I.Dessouky, et al., “Speech enhancement with an
adaptive Wiener filter”, International Journal on Speech Technology,
vol. 17, pp.53–64, 2014.
[12] S.Haykin, Adaptive Filter Theory. Prentice-Hall, 2001.
[13] S.Haykin, Least-Mean-Square Adaptive Filters. Wiley, 2003.
[14] J.Hellgren, “Analysis of Feedback Cancellation in Hearing Aids with
Filtered-X LMS and the Direct Method of Closed Loop Identification”,
IEEE Transactions on Speech and Audio Processing, vol. 10, no.2,
pp.119-131, 2002.
[15] B.Huang, Y.Xiao, J.Sun and G.Wei, “A Variable Step-Size Fx-LMS
Algorithm for Narrowband Active Noise Control”, IEEE Transactions
on Audio, Speech, and Language Processing, vol. 21, no.2, pp.301-312,
2013.
[16] H.C.Huang and J.Lee, “A New Variable Step-Size NLMS Algorithm
and Its Performance Analysis”, IEEE Transactions on Signal
Processing, vol. 60, no.4, pp.2055-2060, 2012.
[17] Y.Lu and P.C.Loizou, “A geometric approach to Spectral subtraction”,
Speech Communication, vol. 50, pp.453-466, 2008.
[18] J.R.Mohammed, M.S.Shafi, S.Imtiaz, R.I.Ansari and M.Khan, “An
Efficient Adaptive Noise Cancellation Scheme Using ALE and NLMS
Filters”, International Journal of Electrical and Computer Engineering,
vol. 2, no.3, pp. 325-332, 2012.
[19] B.K.Mohanty and P.K.Meher, “A High Performance Energy-Efficient
Architecture for FIR Adaptive Filter Based on New Distributed
Arithmetic Formulation of Block LMS Algorithm”, IEEE Transactions
on Signal Processing, vol. 61, no.4, pp.921-932, 2013.
[20] R.Serizel, M.Moonen, J.Wouters and S.H.Jensen, “A Zone-of-Quiet
Based Approach to Integrated Active Noise Control and Noise
Reduction for Speech Enhancement in Hearing Aids”, IEEE
Transactions on Audio, Speech and Language Processing, vol. 20, no.6,
pp.1685-1697, 2012.
[21] B.L.Sim, Y.C.Tong, J.Chang and C.T.Tan, “A parametric formulation of
the generalized spectral subtraction method”, IEEE Transactions on
Speech and Audio Processing, vol. 6, no.4, pp.328–337, 1998.
@article{"International Journal of Electrical, Electronic and Communication Sciences:68648", author = "M. Kalamani and S. Valarmathy and M. Krishnamoorthi", title = "Adaptive Noise Reduction Algorithm for Speech Enhancement", abstract = "In this paper, Least Mean Square (LMS) adaptive
noise reduction algorithm is proposed to enhance the speech signal
from the noisy speech. In this, the speech signal is enhanced by
varying the step size as the function of the input signal. Objective and
subjective measures are made under various noises for the proposed
and existing algorithms. From the experimental results, it is seen that
the proposed LMS adaptive noise reduction algorithm reduces Mean
square Error (MSE) and Log Spectral Distance (LSD) as compared to
that of the earlier methods under various noise conditions with
different input SNR levels. In addition, the proposed algorithm
increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR
improvement (ΔSNRseg) values; improves the Mean Opinion Score
(MOS) as compared to that of the various existing LMS adaptive
noise reduction algorithms. From these experimental results, it is
observed that the proposed LMS adaptive noise reduction algorithm
reduces the speech distortion and residual noise as compared to that
of the existing methods.
", keywords = "LMS, speech enhancement, speech quality, residual
noise.", volume = "8", number = "6", pages = "1014-8", }
